Overview

Dataset statistics

Number of variables16
Number of observations8998
Missing cells552
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory122.0 B

Variable types

Numeric11
Boolean1
Categorical4

Alerts

age is highly correlated with income and 3 other fieldsHigh correlation
income is highly correlated with age and 3 other fieldsHigh correlation
frq is highly correlated with age and 4 other fieldsHigh correlation
mnt is highly correlated with age and 4 other fieldsHigh correlation
clothes is highly correlated with kitchen and 3 other fieldsHigh correlation
kitchen is highly correlated with clothesHigh correlation
small_appliances is highly correlated with clothesHigh correlation
toys is highly correlated with clothesHigh correlation
house_keeping is highly correlated with clothesHigh correlation
dependents is highly correlated with frq and 2 other fieldsHigh correlation
per_net_purchase is highly correlated with age and 4 other fieldsHigh correlation
dependents has 282 (3.1%) missing values Missing
status has 177 (2.0%) missing values Missing
kitchen has 833 (9.3%) zeros Zeros
toys has 815 (9.1%) zeros Zeros
house_keeping has 851 (9.5%) zeros Zeros

Reproduction

Analysis started2022-10-02 18:25:56.959618
Analysis finished2022-10-02 18:26:09.864661
Duration12.91 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct61
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1966.05968
Minimum1936
Maximum1996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.4 KiB
2022-10-02T19:26:09.953943image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1936
5-th percentile1939
Q11951
median1966
Q31981
95-th percentile1993
Maximum1996
Range60
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.29655221
Coefficient of variation (CV)0.008797572313
Kurtosis-1.195990117
Mean1966.05968
Median Absolute Deviation (MAD)15
Skewness0.007954084276
Sum17690605
Variance299.1707182
MonotonicityNot monotonic
2022-10-02T19:26:10.049750image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1974171
 
1.9%
1951169
 
1.9%
1992168
 
1.9%
1979167
 
1.9%
1976166
 
1.8%
1961165
 
1.8%
1960164
 
1.8%
1949163
 
1.8%
1959162
 
1.8%
1978160
 
1.8%
Other values (51)7343
81.6%
ValueCountFrequency (%)
193675
0.8%
1937140
1.6%
1938146
1.6%
1939137
1.5%
1940153
1.7%
1941143
1.6%
1942155
1.7%
1943142
1.6%
1944152
1.7%
1945142
1.6%
ValueCountFrequency (%)
199681
0.9%
1995147
1.6%
1994159
1.8%
1993157
1.7%
1992168
1.9%
1991141
1.6%
1990133
1.5%
1989152
1.7%
1988150
1.7%
1987136
1.5%

income
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8524
Distinct (%)95.2%
Missing46
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean69963.55083
Minimum10000
Maximum140628
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.4 KiB
2022-10-02T19:26:10.157681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile26314.6
Q147741
median70030.5
Q392218
95-th percentile113395.3
Maximum140628
Range130628
Interquartile range (IQR)44477

Descriptive statistics

Standard deviation27591.55623
Coefficient of variation (CV)0.3943704386
Kurtosis-0.9293280359
Mean69963.55083
Median Absolute Deviation (MAD)22214.5
Skewness0.008688890946
Sum626313707
Variance761293975
MonotonicityNot monotonic
2022-10-02T19:26:10.268730image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000035
 
0.4%
641854
 
< 0.1%
499483
 
< 0.1%
379023
 
< 0.1%
661843
 
< 0.1%
994523
 
< 0.1%
397823
 
< 0.1%
834553
 
< 0.1%
832673
 
< 0.1%
517433
 
< 0.1%
Other values (8514)8889
98.8%
(Missing)46
 
0.5%
ValueCountFrequency (%)
1000035
0.4%
101821
 
< 0.1%
101861
 
< 0.1%
106081
 
< 0.1%
108861
 
< 0.1%
113471
 
< 0.1%
114371
 
< 0.1%
114741
 
< 0.1%
117191
 
< 0.1%
117601
 
< 0.1%
ValueCountFrequency (%)
1406281
< 0.1%
1373381
< 0.1%
1370531
< 0.1%
1369221
< 0.1%
1362131
< 0.1%
1361921
< 0.1%
1359591
< 0.1%
1357891
< 0.1%
1355791
< 0.1%
1346891
< 0.1%

frq
Real number (ℝ≥0)

HIGH CORRELATION

Distinct57
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.84807735
Minimum3
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.4 KiB
2022-10-02T19:26:10.379373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile7
Q110
median17
Q328
95-th percentile40
Maximum59
Range56
Interquartile range (IQR)18

Descriptive statistics

Standard deviation10.90343461
Coefficient of variation (CV)0.549344625
Kurtosis-0.4139889171
Mean19.84807735
Median Absolute Deviation (MAD)8
Skewness0.6977790699
Sum178593
Variance118.8848863
MonotonicityNot monotonic
2022-10-02T19:26:10.478915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10635
 
7.1%
9583
 
6.5%
11513
 
5.7%
8493
 
5.5%
12418
 
4.6%
7325
 
3.6%
13316
 
3.5%
14282
 
3.1%
21238
 
2.6%
16233
 
2.6%
Other values (47)4962
55.1%
ValueCountFrequency (%)
35
 
0.1%
424
 
0.3%
587
 
1.0%
6173
 
1.9%
7325
3.6%
8493
5.5%
9583
6.5%
10635
7.1%
11513
5.7%
12418
4.6%
ValueCountFrequency (%)
592
 
< 0.1%
581
 
< 0.1%
571
 
< 0.1%
563
 
< 0.1%
553
 
< 0.1%
543
 
< 0.1%
536
 
0.1%
528
0.1%
5115
0.2%
509
0.1%

rcn
Real number (ℝ≥0)

Distinct378
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.46977106
Minimum0
Maximum549
Zeros44
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size70.4 KiB
2022-10-02T19:26:10.579490image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q126
median53
Q379
95-th percentile99
Maximum549
Range549
Interquartile range (IQR)53

Descriptive statistics

Standard deviation69.76180219
Coefficient of variation (CV)1.116728956
Kurtosis21.09692287
Mean62.46977106
Median Absolute Deviation (MAD)26
Skewness4.174006567
Sum562103
Variance4866.709045
MonotonicityNot monotonic
2022-10-02T19:26:10.668130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9107
 
1.2%
56105
 
1.2%
64103
 
1.1%
29102
 
1.1%
4102
 
1.1%
27100
 
1.1%
92100
 
1.1%
6899
 
1.1%
5499
 
1.1%
1798
 
1.1%
Other values (368)7983
88.7%
ValueCountFrequency (%)
044
0.5%
191
1.0%
292
1.0%
391
1.0%
4102
1.1%
569
0.8%
686
1.0%
780
0.9%
877
0.9%
9107
1.2%
ValueCountFrequency (%)
5493
< 0.1%
5471
 
< 0.1%
5463
< 0.1%
5422
< 0.1%
5401
 
< 0.1%
5381
 
< 0.1%
5371
 
< 0.1%
5351
 
< 0.1%
5341
 
< 0.1%
5331
 
< 0.1%

mnt
Real number (ℝ≥0)

HIGH CORRELATION

Distinct717
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean622.162814
Minimum6
Maximum3052
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.4 KiB
2022-10-02T19:26:10.764936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile19
Q163
median383
Q31076
95-th percentile1917.15
Maximum3052
Range3046
Interquartile range (IQR)1013

Descriptive statistics

Standard deviation646.7682046
Coefficient of variation (CV)1.039548154
Kurtosis-0.05809376933
Mean622.162814
Median Absolute Deviation (MAD)343
Skewness0.9809806035
Sum5598221
Variance418309.1104
MonotonicityNot monotonic
2022-10-02T19:26:10.856233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19158
 
1.8%
41121
 
1.3%
20108
 
1.2%
4089
 
1.0%
4288
 
1.0%
6486
 
1.0%
6678
 
0.9%
6576
 
0.8%
9261
 
0.7%
11856
 
0.6%
Other values (707)8077
89.8%
ValueCountFrequency (%)
61
 
< 0.1%
72
 
< 0.1%
88
 
0.1%
914
 
0.2%
1022
0.2%
1126
0.3%
1223
0.3%
1328
0.3%
1437
0.4%
1526
0.3%
ValueCountFrequency (%)
30521
< 0.1%
29381
< 0.1%
29361
< 0.1%
28781
< 0.1%
28231
< 0.1%
28211
< 0.1%
27052
< 0.1%
27042
< 0.1%
27031
< 0.1%
26482
< 0.1%

clothes
Real number (ℝ≥0)

HIGH CORRELATION

Distinct99
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.44665481
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.4 KiB
2022-10-02T19:26:10.954416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q133
median51
Q369
95-th percentile88
Maximum99
Range98
Interquartile range (IQR)36

Descriptive statistics

Standard deviation23.42224892
Coefficient of variation (CV)0.4642973653
Kurtosis-0.9185954232
Mean50.44665481
Median Absolute Deviation (MAD)18
Skewness-0.07821931254
Sum453919
Variance548.6017444
MonotonicityNot monotonic
2022-10-02T19:26:11.055436image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55150
 
1.7%
40141
 
1.6%
47139
 
1.5%
41139
 
1.5%
70137
 
1.5%
56136
 
1.5%
58136
 
1.5%
46135
 
1.5%
57133
 
1.5%
31133
 
1.5%
Other values (89)7619
84.7%
ValueCountFrequency (%)
12
 
< 0.1%
215
 
0.2%
324
 
0.3%
438
0.4%
544
0.5%
653
0.6%
755
0.6%
857
0.6%
970
0.8%
1064
0.7%
ValueCountFrequency (%)
991
 
< 0.1%
981
 
< 0.1%
979
 
0.1%
9617
 
0.2%
9525
 
0.3%
9436
0.4%
9356
0.6%
9246
0.5%
9160
0.7%
9064
0.7%

kitchen
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct58
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.039675483
Minimum0
Maximum75
Zeros833
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size70.4 KiB
2022-10-02T19:26:11.162616image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q310
95-th percentile23
Maximum75
Range75
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.84813931
Coefficient of variation (CV)1.114843906
Kurtosis5.619265964
Mean7.039675483
Median Absolute Deviation (MAD)3
Skewness2.049458185
Sum63343
Variance61.59329064
MonotonicityNot monotonic
2022-10-02T19:26:11.266097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11326
14.7%
21005
11.2%
0833
 
9.3%
3761
 
8.5%
4644
 
7.2%
5584
 
6.5%
6506
 
5.6%
7404
 
4.5%
8372
 
4.1%
10289
 
3.2%
Other values (48)2274
25.3%
ValueCountFrequency (%)
0833
9.3%
11326
14.7%
21005
11.2%
3761
8.5%
4644
7.2%
5584
6.5%
6506
 
5.6%
7404
 
4.5%
8372
 
4.1%
9277
 
3.1%
ValueCountFrequency (%)
751
< 0.1%
671
< 0.1%
651
< 0.1%
611
< 0.1%
591
< 0.1%
581
< 0.1%
551
< 0.1%
511
< 0.1%
501
< 0.1%
492
< 0.1%

small_appliances
Real number (ℝ≥0)

HIGH CORRELATION

Distinct73
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.52411647
Minimum1
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.4 KiB
2022-10-02T19:26:11.377610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q119
median28
Q337
95-th percentile50
Maximum74
Range73
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.5864368
Coefficient of variation (CV)0.4412559742
Kurtosis-0.4230030191
Mean28.52411647
Median Absolute Deviation (MAD)9
Skewness0.3146456491
Sum256660
Variance158.4183913
MonotonicityNot monotonic
2022-10-02T19:26:11.478308image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23286
 
3.2%
22277
 
3.1%
26276
 
3.1%
27269
 
3.0%
19268
 
3.0%
25264
 
2.9%
30262
 
2.9%
28261
 
2.9%
31254
 
2.8%
29236
 
2.6%
Other values (63)6345
70.5%
ValueCountFrequency (%)
11
 
< 0.1%
22
 
< 0.1%
313
 
0.1%
439
 
0.4%
553
0.6%
661
0.7%
779
0.9%
8104
1.2%
9131
1.5%
10125
1.4%
ValueCountFrequency (%)
742
 
< 0.1%
731
 
< 0.1%
721
 
< 0.1%
701
 
< 0.1%
693
 
< 0.1%
681
 
< 0.1%
671
 
< 0.1%
667
0.1%
654
 
< 0.1%
6410
0.1%

toys
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct58
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.036897088
Minimum0
Maximum62
Zeros815
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size70.4 KiB
2022-10-02T19:26:11.585746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q310
95-th percentile23
Maximum62
Range62
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.924421742
Coefficient of variation (CV)1.126124433
Kurtosis5.644657211
Mean7.036897088
Median Absolute Deviation (MAD)3
Skewness2.096047402
Sum63318
Variance62.79645995
MonotonicityNot monotonic
2022-10-02T19:26:11.682389image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11370
15.2%
2988
11.0%
0815
 
9.1%
3779
 
8.7%
4675
 
7.5%
5542
 
6.0%
6499
 
5.5%
7409
 
4.5%
8344
 
3.8%
9295
 
3.3%
Other values (48)2282
25.4%
ValueCountFrequency (%)
0815
9.1%
11370
15.2%
2988
11.0%
3779
8.7%
4675
7.5%
5542
 
6.0%
6499
 
5.5%
7409
 
4.5%
8344
 
3.8%
9295
 
3.3%
ValueCountFrequency (%)
621
 
< 0.1%
611
 
< 0.1%
602
< 0.1%
571
 
< 0.1%
561
 
< 0.1%
542
< 0.1%
523
< 0.1%
504
< 0.1%
491
 
< 0.1%
481
 
< 0.1%

house_keeping
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct59
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.929984441
Minimum0
Maximum77
Zeros851
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size70.4 KiB
2022-10-02T19:26:12.034472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q39
95-th percentile23
Maximum77
Range77
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.882655355
Coefficient of variation (CV)1.137470859
Kurtosis6.885521741
Mean6.929984441
Median Absolute Deviation (MAD)3
Skewness2.229124081
Sum62356
Variance62.13625544
MonotonicityNot monotonic
2022-10-02T19:26:12.132935image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11326
14.7%
2981
10.9%
0851
 
9.5%
3848
 
9.4%
4675
 
7.5%
5519
 
5.8%
6477
 
5.3%
7446
 
5.0%
8357
 
4.0%
9309
 
3.4%
Other values (49)2209
24.5%
ValueCountFrequency (%)
0851
9.5%
11326
14.7%
2981
10.9%
3848
9.4%
4675
7.5%
5519
 
5.8%
6477
 
5.3%
7446
 
5.0%
8357
 
4.0%
9309
 
3.4%
ValueCountFrequency (%)
771
 
< 0.1%
721
 
< 0.1%
621
 
< 0.1%
591
 
< 0.1%
582
< 0.1%
572
< 0.1%
561
 
< 0.1%
553
< 0.1%
522
< 0.1%
501
 
< 0.1%

dependents
Boolean

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing282
Missing (%)3.1%
Memory size17.7 KiB
True
6164 
False
2552 
(Missing)
 
282
ValueCountFrequency (%)
True6164
68.5%
False2552
28.4%
(Missing)282
 
3.1%
2022-10-02T19:26:12.255161image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

per_net_purchase
Real number (ℝ≥0)

HIGH CORRELATION

Distinct82
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.42898422
Minimum4
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.4 KiB
2022-10-02T19:26:12.345087image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile11
Q128
median45
Q357
95-th percentile69
Maximum88
Range84
Interquartile range (IQR)29

Descriptive statistics

Standard deviation18.49574245
Coefficient of variation (CV)0.4359223486
Kurtosis-1.03466056
Mean42.42898422
Median Absolute Deviation (MAD)14
Skewness-0.2664532226
Sum381776
Variance342.0924887
MonotonicityNot monotonic
2022-10-02T19:26:12.451431image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56215
 
2.4%
54214
 
2.4%
57212
 
2.4%
55199
 
2.2%
58192
 
2.1%
61192
 
2.1%
53189
 
2.1%
13188
 
2.1%
60185
 
2.1%
59184
 
2.0%
Other values (72)7028
78.1%
ValueCountFrequency (%)
41
 
< 0.1%
53
 
< 0.1%
615
 
0.2%
735
 
0.4%
854
 
0.6%
988
1.0%
10124
1.4%
11148
1.6%
12166
1.8%
13188
2.1%
ValueCountFrequency (%)
881
 
< 0.1%
841
 
< 0.1%
831
 
< 0.1%
823
 
< 0.1%
813
 
< 0.1%
804
 
< 0.1%
797
 
0.1%
7812
0.1%
7713
0.1%
7623
0.3%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size70.4 KiB
M
5784 
F
3214 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8998
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowM
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
M5784
64.3%
F3214
35.7%

Length

2022-10-02T19:26:12.541555image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T19:26:12.620597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
m5784
64.3%
f3214
35.7%

Most occurring characters

ValueCountFrequency (%)
M5784
64.3%
F3214
35.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter8998
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M5784
64.3%
F3214
35.7%

Most occurring scripts

ValueCountFrequency (%)
Latin8998
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M5784
64.3%
F3214
35.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII8998
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M5784
64.3%
F3214
35.7%

education
Categorical

Distinct6
Distinct (%)0.1%
Missing47
Missing (%)0.5%
Memory size70.4 KiB
Graduation
4429 
2nd Cycle
1496 
Master
1303 
1st Cycle
1104 
PhD
593 

Length

Max length10
Median length9
Mean length8.660596581
Min length3

Characters and Unicode

Total characters77521
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGraduation
2nd rowPhD
3rd rowGraduation
4th rowMaster
5th rowGraduation

Common Values

ValueCountFrequency (%)
Graduation4429
49.2%
2nd Cycle1496
 
16.6%
Master1303
 
14.5%
1st Cycle1104
 
12.3%
PhD593
 
6.6%
OldSchool26
 
0.3%
(Missing)47
 
0.5%

Length

2022-10-02T19:26:12.715230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T19:26:12.827129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
graduation4429
38.3%
cycle2600
22.5%
2nd1496
 
13.0%
master1303
 
11.3%
1st1104
 
9.6%
phd593
 
5.1%
oldschool26
 
0.2%

Most occurring characters

ValueCountFrequency (%)
a10161
13.1%
t6836
 
8.8%
d5951
 
7.7%
n5925
 
7.6%
r5732
 
7.4%
o4481
 
5.8%
G4429
 
5.7%
u4429
 
5.7%
i4429
 
5.7%
e3903
 
5.0%
Other values (14)21245
27.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter62751
80.9%
Uppercase Letter9570
 
12.3%
Space Separator2600
 
3.4%
Decimal Number2600
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a10161
16.2%
t6836
10.9%
d5951
9.5%
n5925
9.4%
r5732
9.1%
o4481
7.1%
u4429
7.1%
i4429
7.1%
e3903
 
6.2%
l2652
 
4.2%
Other values (4)8252
13.2%
Uppercase Letter
ValueCountFrequency (%)
G4429
46.3%
C2600
27.2%
M1303
 
13.6%
P593
 
6.2%
D593
 
6.2%
O26
 
0.3%
S26
 
0.3%
Decimal Number
ValueCountFrequency (%)
21496
57.5%
11104
42.5%
Space Separator
ValueCountFrequency (%)
2600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin72321
93.3%
Common5200
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a10161
14.0%
t6836
9.5%
d5951
 
8.2%
n5925
 
8.2%
r5732
 
7.9%
o4481
 
6.2%
G4429
 
6.1%
u4429
 
6.1%
i4429
 
6.1%
e3903
 
5.4%
Other values (11)16045
22.2%
Common
ValueCountFrequency (%)
2600
50.0%
21496
28.8%
11104
21.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII77521
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a10161
13.1%
t6836
 
8.8%
d5951
 
7.7%
n5925
 
7.6%
r5732
 
7.4%
o4481
 
5.8%
G4429
 
5.7%
u4429
 
5.7%
i4429
 
5.7%
e3903
 
5.0%
Other values (14)21245
27.4%

status
Categorical

MISSING

Distinct6
Distinct (%)0.1%
Missing177
Missing (%)2.0%
Memory size70.4 KiB
Married
3273 
Single
2293 
Together
2118 
Divorced
677 
Widow
445 

Length

Max length8
Median length7
Mean length6.957714545
Min length5

Characters and Unicode

Total characters61374
Distinct characters19
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTogether
2nd rowDivorced
3rd rowMarried
4th rowTogether
5th rowSingle

Common Values

ValueCountFrequency (%)
Married3273
36.4%
Single2293
25.5%
Together2118
23.5%
Divorced677
 
7.5%
Widow445
 
4.9%
Whatever15
 
0.2%
(Missing)177
 
2.0%

Length

2022-10-02T19:26:12.924489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T19:26:13.026151image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
married3273
37.1%
single2293
26.0%
together2118
24.0%
divorced677
 
7.7%
widow445
 
5.0%
whatever15
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e10509
17.1%
r9356
15.2%
i6688
10.9%
g4411
 
7.2%
d4395
 
7.2%
a3288
 
5.4%
M3273
 
5.3%
o3240
 
5.3%
l2293
 
3.7%
n2293
 
3.7%
Other values (9)11628
18.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter52553
85.6%
Uppercase Letter8821
 
14.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e10509
20.0%
r9356
17.8%
i6688
12.7%
g4411
8.4%
d4395
8.4%
a3288
 
6.3%
o3240
 
6.2%
l2293
 
4.4%
n2293
 
4.4%
t2133
 
4.1%
Other values (4)3947
 
7.5%
Uppercase Letter
ValueCountFrequency (%)
M3273
37.1%
S2293
26.0%
T2118
24.0%
D677
 
7.7%
W460
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Latin61374
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e10509
17.1%
r9356
15.2%
i6688
10.9%
g4411
 
7.2%
d4395
 
7.2%
a3288
 
5.4%
M3273
 
5.3%
o3240
 
5.3%
l2293
 
3.7%
n2293
 
3.7%
Other values (9)11628
18.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII61374
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e10509
17.1%
r9356
15.2%
i6688
10.9%
g4411
 
7.2%
d4395
 
7.2%
a3288
 
5.4%
M3273
 
5.3%
o3240
 
5.3%
l2293
 
3.7%
n2293
 
3.7%
Other values (9)11628
18.9%

description
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size70.4 KiB
OK nice!
3434 
Meh...
2107 
Kind of OK
2090 
Take my money!!
1326 
Horrible
 
41

Length

Max length15
Median length10
Mean length9.027783952
Min length6

Characters and Unicode

Total characters81232
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTake my money!!
2nd rowTake my money!!
3rd rowKind of OK
4th rowOK nice!
5th rowTake my money!!

Common Values

ValueCountFrequency (%)
OK nice!3434
38.2%
Meh...2107
23.4%
Kind of OK2090
23.2%
Take my money!!1326
 
14.7%
Horrible41
 
0.5%

Length

2022-10-02T19:26:13.114904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-02T19:26:13.205150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
ok5524
28.7%
nice3434
17.8%
meh2107
 
10.9%
kind2090
 
10.8%
of2090
 
10.8%
take1326
 
6.9%
my1326
 
6.9%
money1326
 
6.9%
horrible41
 
0.2%

Most occurring characters

ValueCountFrequency (%)
10266
12.6%
e8234
10.1%
K7614
9.4%
n6850
 
8.4%
.6321
 
7.8%
!6086
 
7.5%
i5565
 
6.9%
O5524
 
6.8%
o3457
 
4.3%
c3434
 
4.2%
Other values (13)17881
22.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter41947
51.6%
Uppercase Letter16612
 
20.5%
Other Punctuation12407
 
15.3%
Space Separator10266
 
12.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e8234
19.6%
n6850
16.3%
i5565
13.3%
o3457
8.2%
c3434
8.2%
m2652
 
6.3%
y2652
 
6.3%
h2107
 
5.0%
d2090
 
5.0%
f2090
 
5.0%
Other values (5)2816
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
K7614
45.8%
O5524
33.3%
M2107
 
12.7%
T1326
 
8.0%
H41
 
0.2%
Other Punctuation
ValueCountFrequency (%)
.6321
50.9%
!6086
49.1%
Space Separator
ValueCountFrequency (%)
10266
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin58559
72.1%
Common22673
 
27.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e8234
14.1%
K7614
13.0%
n6850
11.7%
i5565
9.5%
O5524
9.4%
o3457
 
5.9%
c3434
 
5.9%
m2652
 
4.5%
y2652
 
4.5%
M2107
 
3.6%
Other values (10)10470
17.9%
Common
ValueCountFrequency (%)
10266
45.3%
.6321
27.9%
!6086
26.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII81232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10266
12.6%
e8234
10.1%
K7614
9.4%
n6850
 
8.4%
.6321
 
7.8%
!6086
 
7.5%
i5565
 
6.9%
O5524
 
6.8%
o3457
 
4.3%
c3434
 
4.2%
Other values (13)17881
22.0%

Interactions

2022-10-02T19:26:08.216280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:25:57.924858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:25:58.821769image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:25:59.754923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:00.740543image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:01.832356image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:02.798494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:03.867402image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:04.870463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:05.878955image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:07.068442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:08.307693image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:25:58.011297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:25:58.902727image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:25:59.834505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:00.823836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:01.919484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:02.879303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:03.949865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:04.958227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:05.964437image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:07.159013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:08.401397image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:25:58.099575image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:25:58.991374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:25:59.919067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:00.910160image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:02.003874image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:02.962603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:04.035937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:05.055105image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:06.054009image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:07.259226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:08.507966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:25:58.176827image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:25:59.074323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:00.000119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:00.991583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:02.086892image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:03.047507image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:04.117682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:05.146538image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:06.137441image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:07.352368image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:08.614242image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:25:58.253381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:25:59.157005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:00.090062image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:01.074635image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:02.167574image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:03.130037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:04.206659image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:05.238362image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:06.219898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:07.442330image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:08.718726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:25:58.333399image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:25:59.240691image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:00.219886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:01.160295image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:02.251199image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:03.213341image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:04.301930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:05.332418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:06.315316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:07.541767image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:08.806055image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:25:58.410271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:25:59.326541image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:00.302666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:01.247991image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:02.339592image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:03.295410image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:04.413143image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:05.424420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:06.581095image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:07.641351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:08.898280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:25:58.491437image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:25:59.416227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:00.390146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:01.336349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:02.429486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:03.387340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:04.505548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:05.516656image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:06.707666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:07.760363image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:08.997016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:25:58.573399image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:25:59.505852image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:00.478968image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:01.575254image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:02.519279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:03.489154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:04.599621image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:05.610216image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:06.796867image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:07.873256image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:09.092419image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:25:58.655283image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:25:59.590518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:00.566353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:01.659207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:02.604186image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:03.608754image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:04.690224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:05.698016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:06.889233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:07.985735image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:09.190875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:25:58.742634image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:25:59.676841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:00.656418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:01.747275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:02.718630image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:03.745304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:04.784979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:05.794653image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:06.983186image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-02T19:26:08.107140image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-10-02T19:26:13.293393image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Missing values

2022-10-02T19:26:09.338080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-02T19:26:09.540486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-02T19:26:09.681917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-10-02T19:26:09.756174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

ageincomefrqrcnmntclotheskitchensmall_appliancestoyshouse_keepingdependentsper_net_purchasegendereducationstatusdescription
0194690782.03366140237544103False19MGraduationTogetherTake my money!!
11936113023.032615375513842False9FPhDDivorcedTake my money!!
2199028344.0116944321924124True59MGraduationMarriedKind of OK
3195593571.0261088860101965True35FMasterNaNOK nice!
4195591852.0312611385952844True34FGraduationTogetherTake my money!!
5198222386.01465564724821True67MPhDSingleOK nice!
6196969485.018733457171318True46MGraduationTogetherOK nice!
7196068602.0544418411220True37MGraduationTogetherHorrible
81940109499.0307514013893599False17MGraduationDivorcedOK nice!
9199423846.0815319185517101True39F1st CycleTogetherMeh...

Last rows

ageincomefrqrcnmntclotheskitchensmall_appliancestoyshouse_keepingdependentsper_net_purchasegendereducationstatusdescription
89881947100928.028611527432012False29FMasterDivorcedTake my money!!
8989194787605.0211882334219351False9M1st CycleWidowKind of OK
8990199528144.0104146114024222True59M1st CycleMarriedOK nice!
89911939126254.0463622313244798False22MGraduationDivorcedTake my money!!
8992195487399.02518375682781<NA>47MGraduationMarriedKind of OK
8993196094367.02818966852134True55F1st CycleSingleTake my money!!
8994197558121.0126615362876True71M2nd CycleSingleMeh...
8995198654292.029721011411136111False31MGraduationTogetherTake my money!!
89961938125962.0387516686122556True45M2nd CycleMarriedTake my money!!
8997199426385.092446513214615True52M1st CycleSingleKind of OK